Logic Minimization

Logic minimization aims to simplify Boolean formulas or circuits, reducing their size and complexity while preserving functionality. Current research focuses on leveraging machine learning, particularly graph neural networks and reinforcement learning, to guide the search for optimal solutions, often integrating these approaches with traditional Boolean algebra techniques and heuristic-based methods. These advancements improve the efficiency and scalability of logic synthesis, a crucial step in electronic design automation, leading to smaller, faster, and more energy-efficient integrated circuits. The development of novel algorithms and the application of advanced search strategies are key drivers in this ongoing effort.